tunerconfig

Fusion filter tuner configuration options

Since R2020b

Description

The tunerconfig object creates a tuner configuration for a fusion filter used to tune the filter for reduced estimation error.

Creation

Syntax

config = tunerconfig(filterName)

config = tunerconfig(filter)

config = tunerconfig(filterName,Name,Value)

Description

config = tunerconfig(filterName) creates a tunerconfig object controlling the optimization algorithm of the tune function of the fusion filter by specifying a filter name.

config = tunerconfig(filter) creates a tunerconfig object controlling the optimization algorithm of the tune function of the fusion filter by specifying a filter object.

config = tunerconfig(filterName,Name,Value) configures the created tunerconfig object properties using one or more name-value pair arguments. Name is a property name and Value is the corresponding value. Name must appear inside quotes. You can specify several name-value pair arguments in any order as Name1,Value1,...,NameN,ValueN. Any unspecified properties take default values.

For example, tunerconfig('imufilter','MaxIterations',3) create a tunerconfig object for the imufilter object with the a maximum of three allowed iterations.

Input Arguments

filterName — Fusion filter name

'imufilter' | 'ahrsfilter' | 'ahrs10filter' | 'insfilterAsync' | 'insfilterMARG' | 'insfitlerErrorState' | 'insfilterNonholonomic'

Fusion filter name, specified as one of these options:

• 'imufilter'

• 'ahrsfilter'

• 'ahrs10filter'

• 'insfilterAsync'

• 'insfilterMARG'

• 'insfitlerErrorState'

• 'insfilterNonholonomic'

filter — Fusion filter

fusion filter object

Fusion filter, specified as one of these fusion filter objects:

• insEKF

• ahrs10filter

• insfilterAsync

• insfilterMARG

• insfilterErrorState

• insfilterNonholonomic

• ahrsfilter

• imufilter

.

Properties

Filter — Class name of filter

string

This property is read-only.

Class name of filter, specified as a string. Its value is one of these strings:

• "imufilter"

• "ahrsfilter"

• "ahrs10filter"

• "insfilterAsync"

• "insfilterMARG"

• "insfitlerErrorState"

• "insfilterNonholonomic"

TunableParameters — Tunable parameters

array of string (default) | cell array

Tunable parameters, specified as an array of strings or a cell array.

• If you want to tune all the elements in each parameter together (scaling up or down all the elements in a process noise matrix for example), then specify the property as an array of strings. Each string corresponds to a property name.

  For filter objects other than the insEKF object, this is the default option. With the default option, the property contains all the tunable parameter names as an array of strings. Each string is a tunable property name of the fusion filter.

• If you want to tune a subset of elements for at least one noise parameter, specify it as a cell array. The number of cells is the number of parameters that you want to tune.

  • You can specify any cell element as a character vector, representing the property that you want to tune. In this case, the filter tunes all the elements in the property together.

  • You can also specify any cell element as a 1-by-2 cell array, in which the first cell is a character vector, representing the property that you want tune. The second cell in the cell array is a vector of indices, representing the elements that you want to tune in the property. These indices are column-based indices.

  This is default option for the insEKF object.

  For example, running the following:

  >> filter = insEKF;
  config = tunerconfig(filter);
  tunable = config.TunableParameters

  and you can obtain:

  
  tunable =
  
    1×3 cell array
  
      {1×2 cell}    {'AccelerometerNoise'}    {'GyroscopeNoise'}
  
  >> firstCell = tunable{1}
  
  firstCell =
  
    1×2 cell array
  
      {'AdditiveProcessNoise'}    {[1 15 29 43 57 71 85 99 113 127 141 155 169]}

  In the filter, the additive process noise matrix is a 13-by-13 matrices, and the column-based indices represent all the diagonal elements of the matrix.

Example: ["AccelerometerNoise" "GyroscopeNoise"]

StepForward — Factor of forward step

1.1 (default) | scalar larger than 1

Factor of a forward step, specified as a scalar larger than 1. During the tuning process, the tuner increases or decreases the noise parameters to achieve smaller estimation errors. This property specifies the ratio of parameter increase during a parameter increase step.

StepBackward — Factor of backward step

0.5 (default) | scalar in range (0,1)

Factor of a backward step, specified as a scalar in the range of (0,1). During the tuning process, the tuner increases or decreases the noise parameters to achieve smaller estimation errors. This property specifies the factor of parameter decrease during a parameter decrease step.

MaxIterations — Maximum number of iterations

20 (default) | positive integer

Maximum number of iterations allowed by the tuning algorithm, specified as a positive integer.

ObjectiveLimit — Cost at which to stop tuning process

0.1 (default) | positive scalar

Cost at which to stop the tuning process, specified as a positive scalar.

FunctionTolerance — Minimum change in cost to continue tuning

0 (default) | nonnegative scalar

Minimum change in cost to continue tuning, specified as a nonnegative scalar. If the change in cost is smaller than the specified tolerance, the tuning process stops.

Display — Enable showing the iteration details

"iter" (default) | "none"

Enable showing the iteration details, specified as "iter" or "none". When specified as:

• "iter" — The program shows the tuned parameter details in each iteration in the Command Window.

• "none" — The program does not show any tuning information.

Cost — Metric for evaluating filter performance

"RMS" (default) | "Custom"

Metric for evaluating filter performance, specified as "RMS" or "Custom". When specified as:

• "RMS" — The program optimizes the root-mean-squared (RMS) error between the estimate and the truth.

• "Custom" — The program optimizes the filter performance by using a customized cost function specified by the CustomCostFcn property.

CustomCostFcn — Customized cost function

[] (default) | function handle

Customized cost function, specified as a function handle.

Dependencies

To enable this property, set the Cost property to 'Custom'.

OutputFcn — Output function called at each iteration

[] (default) | function handle

Output function called at each iteration, specified as a function handle. The function must use the following syntax:

stop = myOutputFcn(params,tunerValues)

params is a structure of the current best estimate of each parameter at the end of the current iteration. tunerValues is a structure containing information of the tuner configuration, sensor data, and truth data. It has these fields:

Field Name	Description
Iteration	Iteration count of the tuner, specified as a positive integer
SensorData	Sensor data input to the tune function
GroundTruth	Ground truth input to the tune function
Configuration	tunerconfig object used for tuning
Cost	Tuning cost at the end of the current iteration

Tip

You can use the built-in function tunerPlotPose to visualize the truth data and the estimates for most of your tuning applications. See the Visualize Tuning Results Using tunerPlotPose example for details.

Examples

Create Tunerconfig Object and Show Tunable Parameters

Create a tunerconfig object for the insfilterAsync object.

config = tunerconfig('insfilterAsync')

config = 
  tunerconfig with properties:

               Filter: "insfilterAsync"
    TunableParameters: ["AccelerometerNoise"    "GyroscopeNoise"    "MagnetometerNoise"    "GPSPositionNoise"    "GPSVelocityNoise"    "QuaternionNoise"    "AngularVelocityNoise"    "PositionNoise"    "VelocityNoise"    ...    ] (1x14 string)
          StepForward: 1.1000
         StepBackward: 0.5000
        MaxIterations: 20
       ObjectiveLimit: 0.1000
    FunctionTolerance: 0
              Display: iter
                 Cost: RMS
            OutputFcn: []

Display the default tunable parameters.

config.TunableParameters

ans = 1x14 string
    "AccelerometerNoise"    "GyroscopeNoise"    "MagnetometerNoise"    "GPSPositionNoise"    "GPSVelocityNoise"    "QuaternionNoise"    "AngularVelocityNoise"    "PositionNoise"    "VelocityNoise"    "AccelerationNoise"    "GyroscopeBiasNoise"    "AccelerometerBiasNoise"    "GeomagneticVectorNoise"    "MagnetometerBiasNoise"

Tune insfilterAsync to Optimize Pose Estimate

Load the recorded sensor data and ground truth data.

load('insfilterAsyncTuneData.mat');

Create timetables for the sensor data and the truth data.

sensorData = timetable(Accelerometer, Gyroscope, ...
    Magnetometer, GPSPosition, GPSVelocity, 'SampleRate', 100);
groundTruth = timetable(Orientation, Position, ...
    'SampleRate', 100);

Create an insfilterAsync filter object that has a few noise properties.

filter = insfilterAsync('State', initialState, ...
    'StateCovariance', initialStateCovariance, ...
    'AccelerometerBiasNoise', 1e-7, ...
    'GyroscopeBiasNoise', 1e-7, ...
    'MagnetometerBiasNoise', 1e-7, ...
    'GeomagneticVectorNoise', 1e-7);

Create a tuner configuration object for the filter. Set the maximum iterations to two. Also, set the tunable parameters as the unspecified properties.

config = tunerconfig('insfilterAsync','MaxIterations',8);
config.TunableParameters = setdiff(config.TunableParameters, ...
    {'GeomagneticVectorNoise', 'AccelerometerBiasNoise', ...
    'GyroscopeBiasNoise', 'MagnetometerBiasNoise'});
config.TunableParameters

ans = 1×10 string
    "AccelerationNoise"    "AccelerometerNoise"    "AngularVelocityNoise"    "GPSPositionNoise"    "GPSVelocityNoise"    "GyroscopeNoise"    "MagnetometerNoise"    "PositionNoise"    "QuaternionNoise"    "VelocityNoise"

Use the tuner noise function to obtain a set of initial sensor noises used in the filter.

measNoise = tunernoise('insfilterAsync')

measNoise = struct with fields:
    AccelerometerNoise: 1
        GyroscopeNoise: 1
     MagnetometerNoise: 1
      GPSPositionNoise: 1
      GPSVelocityNoise: 1

Tune the filter and obtain the tuned parameters.

tunedParams = tune(filter,measNoise,sensorData,groundTruth,config);

    Iteration    Parameter               Metric
    _________    _________               ______
    1            AccelerationNoise       2.1345
    1            AccelerometerNoise      2.1264
    1            AngularVelocityNoise    1.9659
    1            GPSPositionNoise        1.9341
    1            GPSVelocityNoise        1.8420
    1            GyroscopeNoise          1.7589
    1            MagnetometerNoise       1.7362
    1            PositionNoise           1.7362
    1            QuaternionNoise         1.7218
    1            VelocityNoise           1.7218
    2            AccelerationNoise       1.7190
    2            AccelerometerNoise      1.7170
    2            AngularVelocityNoise    1.6045
    2            GPSPositionNoise        1.5948
    2            GPSVelocityNoise        1.5323
    2            GyroscopeNoise          1.4803
    2            MagnetometerNoise       1.4703
    2            PositionNoise           1.4703
    2            QuaternionNoise         1.4632
    2            VelocityNoise           1.4632
    3            AccelerationNoise       1.4596
    3            AccelerometerNoise      1.4548
    3            AngularVelocityNoise    1.3923
    3            GPSPositionNoise        1.3810
    3            GPSVelocityNoise        1.3322
    3            GyroscopeNoise          1.2998
    3            MagnetometerNoise       1.2976
    3            PositionNoise           1.2976
    3            QuaternionNoise         1.2943
    3            VelocityNoise           1.2943
    4            AccelerationNoise       1.2906
    4            AccelerometerNoise      1.2836
    4            AngularVelocityNoise    1.2491
    4            GPSPositionNoise        1.2258
    4            GPSVelocityNoise        1.1880
    4            GyroscopeNoise          1.1701
    4            MagnetometerNoise       1.1698
    4            PositionNoise           1.1698
    4            QuaternionNoise         1.1688
    4            VelocityNoise           1.1688
    5            AccelerationNoise       1.1650
    5            AccelerometerNoise      1.1569
    5            AngularVelocityNoise    1.1454
    5            GPSPositionNoise        1.1100
    5            GPSVelocityNoise        1.0778
    5            GyroscopeNoise          1.0709
    5            MagnetometerNoise       1.0675
    5            PositionNoise           1.0675
    5            QuaternionNoise         1.0669
    5            VelocityNoise           1.0669
    6            AccelerationNoise       1.0634
    6            AccelerometerNoise      1.0549
    6            AngularVelocityNoise    1.0549
    6            GPSPositionNoise        1.0180
    6            GPSVelocityNoise        0.9866
    6            GyroscopeNoise          0.9810
    6            MagnetometerNoise       0.9775
    6            PositionNoise           0.9775
    6            QuaternionNoise         0.9768
    6            VelocityNoise           0.9768
    7            AccelerationNoise       0.9735
    7            AccelerometerNoise      0.9652
    7            AngularVelocityNoise    0.9652
    7            GPSPositionNoise        0.9283
    7            GPSVelocityNoise        0.8997
    7            GyroscopeNoise          0.8947
    7            MagnetometerNoise       0.8920
    7            PositionNoise           0.8920
    7            QuaternionNoise         0.8912
    7            VelocityNoise           0.8912
    8            AccelerationNoise       0.8885
    8            AccelerometerNoise      0.8811
    8            AngularVelocityNoise    0.8807
    8            GPSPositionNoise        0.8479
    8            GPSVelocityNoise        0.8238
    8            GyroscopeNoise          0.8165
    8            MagnetometerNoise       0.8165
    8            PositionNoise           0.8165
    8            QuaternionNoise         0.8159
    8            VelocityNoise           0.8159

Fuse the sensor data using the tuned filter.

dt = seconds(diff(groundTruth.Time));
N = size(sensorData,1);
qEst = quaternion.zeros(N,1);
posEst = zeros(N,3);
% Iterate the filter for prediction and correction using sensor data.
for ii=1:N
    if ii ~= 1
        predict(filter, dt(ii-1));
    end
    if all(~isnan(Accelerometer(ii,:)))
        fuseaccel(filter,Accelerometer(ii,:), ...
            tunedParams.AccelerometerNoise);
    end
    if all(~isnan(Gyroscope(ii,:)))
        fusegyro(filter, Gyroscope(ii,:), ...
            tunedParams.GyroscopeNoise);
    end
    if all(~isnan(Magnetometer(ii,1)))
        fusemag(filter, Magnetometer(ii,:), ...
            tunedParams.MagnetometerNoise);
    end
    if all(~isnan(GPSPosition(ii,1)))
        fusegps(filter, GPSPosition(ii,:), ...
            tunedParams.GPSPositionNoise, GPSVelocity(ii,:), ...
            tunedParams.GPSVelocityNoise);
    end
    [posEst(ii,:), qEst(ii,:)] = pose(filter);
end

Compute the RMS errors.

orientationError = rad2deg(dist(qEst, Orientation));
rmsorientationError = sqrt(mean(orientationError.^2))

rmsorientationError = 2.7801

positionError = sqrt(sum((posEst - Position).^2, 2));
rmspositionError = sqrt(mean( positionError.^2))

rmspositionError = 0.5966

Visualize the results.

figure();
t = (0:N-1)./ groundTruth.Properties.SampleRate;
subplot(2,1,1)
plot(t, positionError, 'b');
title("Tuned insfilterAsync" + newline + "Euclidean Distance Position Error")
xlabel('Time (s)');
ylabel('Position Error (meters)')
subplot(2,1,2)
plot(t, orientationError, 'b');
title("Orientation Error")
xlabel('Time (s)');
ylabel('Orientation Error (degrees)');

Tune imufilter to Optimize Orientation Estimate

Load recorded sensor data and ground truth data.

ld = load('imufilterTuneData.mat');
qTrue = ld.groundTruth.Orientation; % true orientation

Create an imufilter object and fuse the filter with the sensor data.

fuse = imufilter;
qEstUntuned = fuse(ld.sensorData.Accelerometer, ...
    ld.sensorData.Gyroscope);

Create a tunerconfig object and tune the imufilter to improve the orientation estimate.

cfg = tunerconfig('imufilter');
tune(fuse, ld.sensorData, ld.groundTruth, cfg);

    Iteration    Parameter                        Metric
    _________    _________                        ______
    1            AccelerometerNoise               0.1149
    1            GyroscopeNoise                   0.1146
    1            GyroscopeDriftNoise              0.1146
    1            LinearAccelerationNoise          0.1122
    1            LinearAccelerationDecayFactor    0.1103
    2            AccelerometerNoise               0.1102
    2            GyroscopeNoise                   0.1098
    2            GyroscopeDriftNoise              0.1098
    2            LinearAccelerationNoise          0.1070
    2            LinearAccelerationDecayFactor    0.1053
    3            AccelerometerNoise               0.1053
    3            GyroscopeNoise                   0.1048
    3            GyroscopeDriftNoise              0.1048
    3            LinearAccelerationNoise          0.1016
    3            LinearAccelerationDecayFactor    0.1002
    4            AccelerometerNoise               0.1001
    4            GyroscopeNoise                   0.0996
    4            GyroscopeDriftNoise              0.0996
    4            LinearAccelerationNoise          0.0962
    4            LinearAccelerationDecayFactor    0.0950
    5            AccelerometerNoise               0.0950
    5            GyroscopeNoise                   0.0943
    5            GyroscopeDriftNoise              0.0943
    5            LinearAccelerationNoise          0.0910
    5            LinearAccelerationDecayFactor    0.0901
    6            AccelerometerNoise               0.0900
    6            GyroscopeNoise                   0.0893
    6            GyroscopeDriftNoise              0.0893
    6            LinearAccelerationNoise          0.0862
    6            LinearAccelerationDecayFactor    0.0855
    7            AccelerometerNoise               0.0855
    7            GyroscopeNoise                   0.0848
    7            GyroscopeDriftNoise              0.0848
    7            LinearAccelerationNoise          0.0822
    7            LinearAccelerationDecayFactor    0.0818
    8            AccelerometerNoise               0.0817
    8            GyroscopeNoise                   0.0811
    8            GyroscopeDriftNoise              0.0811
    8            LinearAccelerationNoise          0.0791
    8            LinearAccelerationDecayFactor    0.0789
    9            AccelerometerNoise               0.0788
    9            GyroscopeNoise                   0.0782
    9            GyroscopeDriftNoise              0.0782
    9            LinearAccelerationNoise          0.0769
    9            LinearAccelerationDecayFactor    0.0768
    10           AccelerometerNoise               0.0768
    10           GyroscopeNoise                   0.0762
    10           GyroscopeDriftNoise              0.0762
    10           LinearAccelerationNoise          0.0754
    10           LinearAccelerationDecayFactor    0.0753
    11           AccelerometerNoise               0.0753
    11           GyroscopeNoise                   0.0747
    11           GyroscopeDriftNoise              0.0747
    11           LinearAccelerationNoise          0.0741
    11           LinearAccelerationDecayFactor    0.0740
    12           AccelerometerNoise               0.0740
    12           GyroscopeNoise                   0.0734
    12           GyroscopeDriftNoise              0.0734
    12           LinearAccelerationNoise          0.0728
    12           LinearAccelerationDecayFactor    0.0728
    13           AccelerometerNoise               0.0728
    13           GyroscopeNoise                   0.0721
    13           GyroscopeDriftNoise              0.0721
    13           LinearAccelerationNoise          0.0715
    13           LinearAccelerationDecayFactor    0.0715
    14           AccelerometerNoise               0.0715
    14           GyroscopeNoise                   0.0706
    14           GyroscopeDriftNoise              0.0706
    14           LinearAccelerationNoise          0.0700
    14           LinearAccelerationDecayFactor    0.0700
    15           AccelerometerNoise               0.0700
    15           GyroscopeNoise                   0.0690
    15           GyroscopeDriftNoise              0.0690
    15           LinearAccelerationNoise          0.0684
    15           LinearAccelerationDecayFactor    0.0684
    16           AccelerometerNoise               0.0684
    16           GyroscopeNoise                   0.0672
    16           GyroscopeDriftNoise              0.0672
    16           LinearAccelerationNoise          0.0668
    16           LinearAccelerationDecayFactor    0.0667
    17           AccelerometerNoise               0.0667
    17           GyroscopeNoise                   0.0655
    17           GyroscopeDriftNoise              0.0655
    17           LinearAccelerationNoise          0.0654
    17           LinearAccelerationDecayFactor    0.0654
    18           AccelerometerNoise               0.0654
    18           GyroscopeNoise                   0.0641
    18           GyroscopeDriftNoise              0.0641
    18           LinearAccelerationNoise          0.0640
    18           LinearAccelerationDecayFactor    0.0639
    19           AccelerometerNoise               0.0639
    19           GyroscopeNoise                   0.0627
    19           GyroscopeDriftNoise              0.0627
    19           LinearAccelerationNoise          0.0627
    19           LinearAccelerationDecayFactor    0.0624
    20           AccelerometerNoise               0.0624
    20           GyroscopeNoise                   0.0614
    20           GyroscopeDriftNoise              0.0614
    20           LinearAccelerationNoise          0.0613
    20           LinearAccelerationDecayFactor    0.0613

Fuse the sensor data again using the tuned filter.

qEstTuned = fuse(ld.sensorData.Accelerometer, ...
    ld.sensorData.Gyroscope);

Compare the tuned and untuned filter RMS error performances.

dUntuned = rad2deg(dist(qEstUntuned, qTrue));
dTuned = rad2deg(dist(qEstTuned, qTrue));
rmsUntuned = sqrt(mean(dUntuned.^2))

rmsUntuned = 6.5864

rmsTuned = sqrt(mean(dTuned.^2))

rmsTuned = 3.5098

Visualize the results.

N = numel(dUntuned);
t = (0:N-1)./ fuse.SampleRate;
plot(t, dUntuned, 'r', t, dTuned, 'b');
legend('Untuned', 'Tuned');
title('imufilter - Tuned vs Untuned Error')
xlabel('Time (s)');
ylabel('Orientation Error (degrees)');

Save Tuned Parameters in MAT File Using Output Function

Load the recorded sensor data and ground truth data.

load('insfilterAsyncTuneData.mat');

Create timetables for the sensor data and the truth data.

sensorData = timetable(Accelerometer, Gyroscope, ...
    Magnetometer, GPSPosition, GPSVelocity, 'SampleRate', 100);
groundTruth = timetable(Orientation, Position, ...
    'SampleRate', 100);

Create an insfilterAsync filter object that has a few noise properties.

filter = insfilterAsync('State', initialState, ...
    'StateCovariance', initialStateCovariance, ...
    'AccelerometerBiasNoise', 1e-7, ...
    'GyroscopeBiasNoise', 1e-7, ...
    'MagnetometerBiasNoise', 1e-7, ...
    'GeomagneticVectorNoise', 1e-7);

Create a tuner configuration object for the filter. Define the OutputFcn property as a customized function, myOutputFcn, which saves the latest tuned parameters in a MAT file.

config = tunerconfig('insfilterAsync', ...
    'MaxIterations',5, ...
    'Display','none', ...
    'OutputFcn', @myOutputFcn);
config.TunableParameters = setdiff(config.TunableParameters, ...
    {'GeomagneticVectorNoise', 'AccelerometerBiasNoise', ...
    'GyroscopeBiasNoise', 'MagnetometerBiasNoise'});
config.TunableParameters

ans = 1x10 string
    "AccelerationNoise"    "AccelerometerNoise"    "AngularVelocityNoise"    "GPSPositionNoise"    "GPSVelocityNoise"    "GyroscopeNoise"    "MagnetometerNoise"    "PositionNoise"    "QuaternionNoise"    "VelocityNoise"

Use the tuner noise function to obtain a set of initial sensor noises used in the filter.

measNoise = tunernoise('insfilterAsync')

measNoise = struct with fields:
    AccelerometerNoise: 1
        GyroscopeNoise: 1
     MagnetometerNoise: 1
      GPSPositionNoise: 1
      GPSVelocityNoise: 1

Tune the filter and obtain the tuned parameters.

tunedParams = tune(filter,measNoise,sensorData,groundTruth,config);

Display the save parameters using the saved file.

fileObject = matfile('myfile.mat');
fileObject.params

ans = struct with fields:
         AccelerationNoise: [88.8995 88.8995 88.8995]
    AccelerometerBiasNoise: [1.0000e-07 1.0000e-07 1.0000e-07]
        AccelerometerNoise: 0.7942
      AngularVelocityNoise: [0.0089 0.0089 0.0089]
          GPSPositionNoise: 1.1664
          GPSVelocityNoise: 0.5210
    GeomagneticVectorNoise: [1.0000e-07 1.0000e-07 1.0000e-07]
        GyroscopeBiasNoise: [1.0000e-07 1.0000e-07 1.0000e-07]
            GyroscopeNoise: 0.5210
     MagnetometerBiasNoise: [1.0000e-07 1.0000e-07 1.0000e-07]
         MagnetometerNoise: 1.0128
             PositionNoise: [5.2100e-07 5.2100e-07 5.2100e-07]
           QuaternionNoise: [1.3239e-06 1.3239e-06 1.3239e-06 1.3239e-06]
         ReferenceLocation: [0 0 0]
                     State: [28x1 double]
           StateCovariance: [28x28 double]
             VelocityNoise: [6.3678e-07 6.3678e-07 6.3678e-07]

The output function

function stop = myOutputFcn(params, ~)
save('myfile.mat','params'); % overwrite the file with latest
stop = false;
end

Version History

Introduced in R2020b